PhD
Chair of Artificial Intelligence and Machine Learning at LMU
Institute of Informatics
Akademiestr. 7
80799 München
Biosketch
Yusuf obtained his Master’s degree in Statistics at the University of Munich (LMU). His studies focused on the theoretical foundations of statistics, particularly in the representation and quantification of uncertainty.
As a student assistant, he gained experience in academic teaching and research early on. After a short stay abroad in France, he joined the Chair of Artificial Intelligence and Machine Learning. Since September, he is a relAI member.
relAI Research
Uncertainty-Aware Machine Learning: Enhancing the Probabilistic Approach
My research focuses on uncertainty in machine learning, especially on the representation and quantification of aleatoric and epistemic uncertainty. A recurring theme in my work is the critical re-evaluation of existing approaches, with the aim of identifying their limitations, clarifying their assumptions, and developing more principled alternatives. In doing so, I seek to deepen our understanding of how uncertainty is represented, quantified, and (empirically) evaluated, while raising awareness of flaws and pitfalls in widely adopted methods.
Publications
https://dl.acm.org/doi/10.5555/3625834.3626047
https://dl.acm.org/doi/10.5555/3692070.3693825
https://dl.acm.org/doi/10.5555/3702676.3702823
https://dl.acm.org/doi/10.48550/arXiv.2503.16809